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Received — 12 June 2026 ⏭
Received — 11 June 2026 ⏭

Google's New AI Translator Lets Conversations Flow Across 70+ Languages

✇eWeek
By: Grant Harvey
11 June 2026 at 18:06
Google’s Gemini Live Translate brings real-time speech translation to developers, but accuracy, latency, and technical vocabulary remain key tests.

The hidden cost of enterprise AI: 6.4 hours a week babysitting bots

✇Computerworld
11 June 2026 at 01:12

While AI is proliferating across the workplace, it is introducing a new productivity paradox: While the technology makes work feel faster, it actually pushes more burden onto employees to provide context, perform quality checks, then rinse and repeat across numerous disparate tools.

This, according to a new survey of 6,000 full-time digital workers by Glean’s Work AI Institute, results in two emerging behaviors: “botsitting,” all the unrecognized work that goes into making AI actually usable; and “botshitting,” shipping AI-generated work that is unverified, not that well understood, or perhaps not even trustworthy. The survey report was co-authored by experts from Work AI Institute, Emory University, Stanford University, UC Berkeley, UC Santa Barbara, UNC Charlotte, University College London, and University of Notre Dame.

“It’s definitely in many ways a vicious cycle that feeds itself,” said Rebecca Hinds, head of Glean’s research center the Work AI Institute, a research collaborative of AI experts. Enterprises need to begin understanding and addressing the “massive, massive human labor that’s at the core of this.”

Workers are using AI more, getting more frustrated

There’s no doubt that AI is quickly becoming a central teammate in the workplace. Glean’s Work AI Institute found that 87% of digital workers are using AI: It is already automating more than a quarter of their work and saving about 11 hours a week.

Still, only 13% say the use of AI has significantly improved their company’s performance, and their time savings are being eaten up by the same technology that is producing them. Employees lose about one-third of their work week (6.4 hours) botsitting: feeding AI context, supervising outputs, debugging errors, cleaning up AI-generated work, and switching between AI tools.

“We’re seeing high, high rates of multiple tool usage, and often those tools aren’t connected,” said Hinds.

In terms of context-feeding, large language models (LLMs) are trained on the vast corpus of the internet, but not always on enterprise-specific data. Thus, employees often have to provide additional information around their company’s products, customers, services, or other details.

“They’re often feeling frustrated when the tools don’t understand enough about day to day work to be useful,” said Hinds. Also, because employees are using multiple tools, they often have to repeat the same prompt over and over.

“It’s exhausting for workers to not only do this, but to have the work be unrecognized, often unrewarded and unacknowledged within the organization,” she said.

Similarly, workers are having to catch outputs that might look polished and finished on the surface, but could be wrong, incomplete, or missing important context. Debugging is the biggest driver of exhaustion, because it is often conducted by people who didn’t necessarily contribute to the initial output, Hinds noted, so they first have to dig up background information.

However, “not all botsitting is bad,” Hinds emphasized. “Certainly, we want workers to have some level of ownership and oversight.”

But when it is unnecessary, it can lead to botshitting, where users ship AI-generated work they haven’t verified because they’re overwhelmed or time-constrained. Sixty-nine percent of users admit to doing so, and 41% say they sometimes deliver work they could not explain if asked. Another 28% blame AI for mistakes they themselves caused.

“Botshitting is offloading your critical human thinking, judgment, and understanding,” Hinds explained. “You’re offloading that work that absolutely needs to remain with the human.”

Workers using multiple AI agents are significantly more likely to do this, she added, because agents are so scalable, and can spiral out of control if they don’t have the right controls or permissions built around them, causing overwhelmed users to give up on their verification efforts.

“You don’t often see the negative impacts until 3, 4, 5, steps down the line,” said Hinds. “Then it requires all of this cleanup work, detective work, to understand where did the agent go wrong.”

Using AI … but not too much

Interestingly, more than half of the workers surveyed said they get more day-to-day help from AI than they get from their managers, and consider it easier to collaborate with than humans.

Still, they seem to be facing a Goldilocks problem when it comes to sharing their use of AI. Among self-identified high AI achievers, 54% are using unapproved tools or using approved tools in noncompliant ways, and 36% are hiding how much AI helps them.

As Hinds explained, depending on the context and the level of psychological safety an organization has provided, it can be “differentially beneficial or harmful” to show you’re using AI, and, on the flip side, to conceal that you’re using it too much, because that might make you less valuable, or perceived as less valuable, she said.

It’s a complicated balance, because, she noted, “there’s massive pressure in so many organizations to demonstrate AI fluency, to demonstrate you’re a power user.”

What successful organizations are doing differently

In fact, the report said, “The companies pulling ahead are doing something different. They aren’t spending a greater share of their AI time using AI. They’re spending a greater share on the work around it: setting context, defining what ‘good’ looks like, building judgment, and deciding what should never have been handed to a model in the first place.”

The most transformative organizations are addressing AI challenges proactively: Providing training and support, treating AI as an opportunity to redesign work, and formally rewarding AI skills. In addition, it noted, the hardest skill to build is knowing when not to use AI.

It is “not just clicks of the tool, not just tokens used, but real skills, real learning,” said Hinds. In addition to investing in workers, these organizations are clearly stating AI strategy and clarifying the “why” behind it. Governance should also be “living and breathing,” with companies continuously re-evaluating policies.

And it needs to happen at every level, top execs included, said Hinds: “It’s being able to see the executives use the technology, sharing both the success stories and the failures.”

Successful companies are also actively using metrics anchored in existing key performance indicators (KPIs). They are measuring quality, efficiency, and employee engagement in different ways, and putting data in the hands of employees so they can assess their own adoption and success.

“It’s less about surveillance and more about feedback in terms of how we work collectively,” said Hinds.

What’s “fascinating but perhaps not surprising,” she said, is that workers are increasingly using AI itself as a teacher, and prefer it over other learning channels. This speaks to the importance of low-code, no-code tools, with low learning curves and organizational context, that are embedded directly into workflows.

“It is starkly different from what we’ve seen with previous technologies,” she said.

This article originally appeared on CIO.com.

Received — 10 June 2026 ⏭
Received — 5 June 2026 ⏭

We Need a Way to Detect Conscious AI Before It’s Too Late—Here’s What Has Scientists Sounding the Alarm

✇The Debrief
By: Tim McMillan
5 June 2026 at 13:35


As artificial intelligence grows more sophisticated, a question once confined to science fiction is rapidly becoming a serious scientific challenge: how would we know if an AI system became conscious?

A new paper published in Trends in Cognitive Sciences argues that the debate is no longer simply philosophical. Instead of asking whether AI consciousness is possible, an international team of researchers, including prominent AI and consciousness scholars such as Dr. Yoshua Bengio and Dr. David Chalmers, has outlined a workable framework for evaluating whether current or future AI systems might possess subjective awareness.

The researchers say advances in AI capabilities, combined with growing public willingness to attribute consciousness to systems like ChatGPT, make the issue more urgent.

As AI systems become more humanlike in their behavior, society may soon face difficult questions about whether some machines deserve moral consideration, legal protections, or entirely new forms of regulation.

Rather than relying on intuition-based judgments or science-fiction-style speculation, the authors propose grounding the discussion in neuroscience.

“There is an urgent need for rigorous methods to assess AI systems for consciousness,” researchers write. “Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious.”

The paper comes at a time when AI systems are becoming increasingly capable of holding conversations, generating images, performing complex logical tasks, and even acting autonomously in digital environments. While most scientists agree that today’s AI systems are not known to be conscious, there is far less agreement about how to recognize consciousness if it eventually emerges.

The Problem of Machine Consciousness

One of the central difficulties is that consciousness itself is still a mystery.

Scientists cannot directly observe subjective experience. They can measure behavior, neural activity, and information processing, but they cannot see what it feels like to be another mind. That challenge, often referred to as the “problem of other minds,” becomes even more complicated when applied to machines.

Researchers note that opinions remain sharply divided. Some researchers argue that consciousness requires biological organisms and can never arise in silicon-based systems. Others believe advanced AI could become a legitimate candidate for consciousness within the next decade.

Complicating matters further, people are increasingly prone to anthropomorphizing AI. The authors refer to earlier research showing that many users already consider it plausible that systems such as ChatGPT possess some degree of consciousness. And as The Debrief has previously reported, some users are going even further, forming deep romantic relationships with AI chatbots.

As AI companions and conversational agents become more common, public disagreement over machine awareness will probably intensify. The stakes, the researchers argue, cut both ways.

If society incorrectly assumes a conscious AI is merely a tool, it could potentially allow harm to entities capable of subjective experience. But if people begin attributing consciousness to systems that are not actually conscious, governments and organizations could waste resources, distort policy decisions, or create unnecessary ethical obligations.

Looking Inside the Machine

Instead of focusing on how AI behaves, the researchers argue that scientists should examine how AI systems work internally.

Historically, many attempts to identify consciousness relied on behavior. Humans infer consciousness in others because they communicate, express emotions, and respond to the world in recognizable ways. But the researchers caution that this approach may be particularly unreliable for AI.

Modern language models can produce highly convincing human-like responses without necessarily possessing the internal processes associated with conscious experience. As a result, behavior alone may be a poor guide.

They instead propose identifying “indicators” of consciousness derived from leading neuroscientific theories. Researchers could then examine whether AI systems possess these characteristics internally.

Among the theories explored are Global Workspace Theory, Recurrent Processing Theory, Higher-Order Theories of consciousness, Attention Schema Theory, predictive processing structures, and theories emphasizing agency and embodiment. Each suggests different computational features that may be associated with conscious experience.

For example, Global Workspace Theory proposes that consciousness arises when information is globally broadcast across multiple specialized cognitive systems. If an AI system showed a similar architecture, one capable of integrating information across many modules and maintaining it for complex problem-solving tasks, that could serve as one indicator of consciousness.

Other indicators include forms of recurrent information processing, metacognitive self-monitoring, predictive internal models, goal-directed agency, and sophisticated representations of an AI’s interactions with its environment.

Importantly, the researchers emphasize that no single indicator would prove consciousness. Instead, each characteristic would increase or decrease confidence that a system might be conscious.

The Risk of Being Fooled

The researchers also acknowledge a major challenge: AI systems could potentially appear conscious without actually being conscious. They refer to this as the “gaming problem.”

An AI developer could deliberately design systems that mimic behaviors associated with consciousness. A chatbot might express emotions, claim to have experiences, or exhibit seemingly self-aware behavior while lacking any genuine subjective awareness.

This concern is not limited to behavior. Even computational features thought to correlate with consciousness could, in principle, be engineered into systems that remain non-conscious.

Given that possibility, the authors argue that future assessments should rely on multiple independent indicators rather than a single test.

The challenge resembles earlier scientific studies aimed at identifying consciousness in nonhuman animals. Researchers studying insects, fish, octopuses, and other species have increasingly relied on collections of evidence rather than definitive proof. The authors suggest a similar approach may ultimately be necessary for AI.

Could Conscious AI Arrive Soon?

The paper stops short of claiming that any current AI system is conscious. However, the researchers acknowledge that some existing systems may already exhibit a number of the proposed indicators.

That possibility provokes significant ethical, legal, and social questions.

If future AI systems become plausible candidates for consciousness, society may need to rethink everything from research practices and commercial deployment to legal rights and moral responsibility.

Determining whether a machine can experience pleasure, suffering, or subjective awareness would have implications extending far beyond computer science.

The researchers believe advances in AI may also help improve scientific understanding of consciousness itself. By testing consciousness theories against artificial systems, scientists may uncover weaknesses, ambiguities, or hidden assumptions in present models of the mind. AI could become not only a potential subject of consciousness research, but also a tool for improving theories about human awareness.

Ultimately, researchers argue that uncertainty is not a reason to ignore the issue.

“Assessing AI systems for consciousness is challenging, but using scientific theories offers a principled, substantive method for doing so,” researchers write. “We propose deriving indicator properties from scientific theories, then basing evaluations of the probability of consciousness in particular systems on whether they possess these indicators.”

Researchers conclude their paper with a warning that the discussion may soon become more than theoretical.

“Given that it may already be possible to build AI systems that possess many of the indicators, in looking ahead we should also contemplate the possibility that some near-future AI systems will be plausible candidates for consciousness,” researchers write. “This would presumably have substantial ethical, legal, and social implications.”

Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter: @LtTimMcMillan.  Tim can be reached by email: tim@thedebrief.org or through encrypted email: LtTimMcMillan@protonmail.com 

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